RoundaboutHD:High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking

News(21/07/2025):

We have submitted this paper to WACV 2026.

RoundaboutHD is a comprehensive, high-resolution multi-camera vehicle tracking (MCVT) dataset captured in a real-world roundabout scenario. It is designed to support the development and benchmarking of object detection, single-camera tracking (SCT), and multi-camera vehicle tracking (MCVT) algorithms in urban environments with nonlinear motion and frequent occlusions.

Dataset Access

The full content of RoundaboutHD is under RoundaboutHD.zip file.

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Dataset Description

RoundaboutHD provides 40 minutes of fully annotated video footage recorded from 4 non-overlapping 4K cameras at 15 FPS. Each camera covers 10 minutes of traffic under various conditions: normal, light, and heavy traffic. The roundabout layout introduces significant challenges such as:

• Nonlinear vehicle trajectories
• Frequent occlusions due to infrastructure (e.g., a central statue)
• Multiple exits and intersections between cameras

This makes RoundaboutHD a challenging yet realistic benchmark for evaluating vehicle tracking performance in smart city applications.

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Dataset Contents

The dataset structure: RoundaboutHD/ ├── imagesc001/ ← Same structure applies to imagesc002, imagesc003, imagesc004 │ ├── detection/ │ │ ├── labels_GT.zip/ │ │ │ ├── img000000.txt │ │ │ ├── ... │ │ │ └── img008999.txt # Total: 9000 files │ │ ├── labels_test.zip/ │ │ └── labels_xy.zip/ │ ├── SCT/ │ │ └── imagesc001_SCT_GT.txt │ ├── geo-mapping/ │ │ ├── cam01_fitted_cam.json │ │ ├── cam01_info.png │ │ ├── cam01_topview.jpg │ │ ├── cam01_trace.png │ │ └── cam01_undistorted.png │ └── video.mp4 ├── imagesc002/ ├── imagesc003/ ├── imagesc004/ ├── ReID_subset/ │ └── ReID_subset.zip/ │ ├── query_images/ │ ├── test_images/ │ └── train_images/ ├── Multi_CAM_Ground_Truth.txt └── vehicle_statistic.xlsx In this dataset, it contains: - Labeled video footage - Object detection Ground Turth - Single-camera tracking (SCT) Ground Turth - camera modelling parameter and visualization - RoundaboutHD image-based ReID subset - Image-based ReID Ground Turth - Multi-camera tracking (MCVT) Ground Turth - Vehicle context information - Evaluation scripts and label format documentation

Evaluation

We provide tools for evaluating tracking performance in this repository:
Multi-Camera Tracking Labelling Tool

Multi-Camera Tracking Evaluation

Use the following command:

python eval_label.py <prediction_file> <ground_truth_file>

Each line in the prediction/ground-truth file should follow this format:

<camera_id> <obj_id> <frame_id> <xmin> <ymin> <width> <height> <xworld> <yworld>

Descriptions:

• camera_id: Integer identifier (1–4 in RoundaboutHD)
• obj_id: Object ID (positive integer, consistent across cameras)
• frame_id: Frame number (starting at 0)
• xmin, ymin, width, height: Bounding box coordinates (pixels)
• xworld, yworld: GPS/world coordinates of the object (optional)

*Note: The value of xworld and yworld do not count into the evaluation, you can use the value -1 as placehold.

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Single-Camera Tracking Evaluation

Use the following command:

python eval_det.py <prediction_file> <ground_truth_file>

Each line in the prediction/ground-truth file should follow this format:

<frame_id> <obj_id> <xmin> <ymin> <xmax> <ymax>

Descriptions: The definition of each item is as same as the multi-camera tracking format.

Object Detection Evaluation

Use the following command:

python eval_sct.py <prediction_directory> <ground_truth_directory>

Each directory should contain multiple .txt files named by frame with format f'img{frame_id:06d}.txt', example as below:

img000000.txt, img000001.txt, ...

Each file must contain object detections in the format:

<class_id> <xmin> <ymin> <xmax> <ymax>

Descriptions: class_id corresponds to the category names pre-defined in the COCO dataset.

*Note: The value of class_id do not influence the results for object detection evaluation.

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Baseline

We evaluate our dataset using ELECTRICITY [1], a general and reproducible MCVT method. We set the distance threshold to 12 and apply a hard removal distance of 80. Static vehicle trajectories are removed. The result is shown below:

Dataset	IDF1	IDP	IDR
RoundaboutHD	28.14	26.45	30.06

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Citation

If you use RoundaboutHD in your research, please cite:

@misc{lin2025roundabouthdhighresolutionrealworldurban,
      title={RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking}, 
      author={Yuqiang Lin and Sam Lockyer and Mingxuan Sui and Li Gan and Florian Stanek and Markus Zarbock and Wenbin Li and Adrian Evans and Nic Zhang},
      year={2025},
      eprint={2507.08729},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.08729}, 
}

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[1] Y. Qian et al., "ELECTRICITY: An Efficient Multi-Camera Vehicle Tracking System for Intelligent City", CVPRW, 2020.